Overview

Dataset statistics

Number of variables26
Number of observations400
Missing cells1009
Missing cells (%)9.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.4 KiB
Average record size in memory208.3 B

Variable types

Numeric11
Categorical9
Text1
Boolean5

Alerts

id is highly overall correlated with albumin and 6 other fieldsHigh correlation
albumin is highly overall correlated with id and 7 other fieldsHigh correlation
sugar is highly overall correlated with blood_glucose_random and 1 other fieldsHigh correlation
blood_glucose_random is highly overall correlated with sugar and 1 other fieldsHigh correlation
blood_urea is highly overall correlated with serum_creatinine and 1 other fieldsHigh correlation
serum_creatinine is highly overall correlated with id and 3 other fieldsHigh correlation
sodium is highly overall correlated with albumin and 1 other fieldsHigh correlation
potassium is highly overall correlated with packed_cell_volume and 1 other fieldsHigh correlation
hemoglobin is highly overall correlated with id and 10 other fieldsHigh correlation
specific_gravity is highly overall correlated with classificationHigh correlation
red_blood_cells is highly overall correlated with id and 3 other fieldsHigh correlation
pus_cell is highly overall correlated with albumin and 4 other fieldsHigh correlation
pus_cell_clumps is highly overall correlated with pus_cellHigh correlation
packed_cell_volume is highly overall correlated with potassium and 7 other fieldsHigh correlation
red_blood_cell_count is highly overall correlated with potassium and 6 other fieldsHigh correlation
hypertension is highly overall correlated with id and 6 other fieldsHigh correlation
diabetes_mellitus is highly overall correlated with id and 7 other fieldsHigh correlation
coronary_artery_disease is highly overall correlated with red_blood_cell_countHigh correlation
anemia is highly overall correlated with hemoglobin and 2 other fieldsHigh correlation
classification is highly overall correlated with id and 8 other fieldsHigh correlation
pus_cell_clumps is highly imbalanced (51.2%)Imbalance
bacteria is highly imbalanced (69.0%)Imbalance
coronary_artery_disease is highly imbalanced (57.9%)Imbalance
age has 9 (2.2%) missing valuesMissing
blood_pressure has 12 (3.0%) missing valuesMissing
specific_gravity has 47 (11.8%) missing valuesMissing
albumin has 46 (11.5%) missing valuesMissing
sugar has 49 (12.2%) missing valuesMissing
red_blood_cells has 152 (38.0%) missing valuesMissing
pus_cell has 65 (16.2%) missing valuesMissing
blood_glucose_random has 44 (11.0%) missing valuesMissing
blood_urea has 19 (4.8%) missing valuesMissing
serum_creatinine has 17 (4.2%) missing valuesMissing
sodium has 87 (21.8%) missing valuesMissing
potassium has 88 (22.0%) missing valuesMissing
hemoglobin has 52 (13.0%) missing valuesMissing
packed_cell_volume has 70 (17.5%) missing valuesMissing
white_blood_cell_count has 105 (26.2%) missing valuesMissing
red_blood_cell_count has 130 (32.5%) missing valuesMissing
id is uniformly distributedUniform
id has unique valuesUnique
albumin has 199 (49.8%) zerosZeros
sugar has 290 (72.5%) zerosZeros

Reproduction

Analysis started2023-11-20 15:08:39.495976
Analysis finished2023-11-20 15:09:01.710798
Duration22.21 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.5
Minimum0
Maximum399
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-11-20T16:09:01.878336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.95
Q199.75
median199.5
Q3299.25
95-th percentile379.05
Maximum399
Range399
Interquartile range (IQR)199.5

Descriptive statistics

Standard deviation115.6143
Coefficient of variation (CV)0.57952031
Kurtosis-1.2
Mean199.5
Median Absolute Deviation (MAD)100
Skewness0
Sum79800
Variance13366.667
MonotonicityStrictly increasing
2023-11-20T16:09:02.464537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.2%
263 1
 
0.2%
273 1
 
0.2%
272 1
 
0.2%
271 1
 
0.2%
270 1
 
0.2%
269 1
 
0.2%
268 1
 
0.2%
267 1
 
0.2%
266 1
 
0.2%
Other values (390) 390
97.5%
ValueCountFrequency (%)
0 1
0.2%
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
ValueCountFrequency (%)
399 1
0.2%
398 1
0.2%
397 1
0.2%
396 1
0.2%
395 1
0.2%
394 1
0.2%
393 1
0.2%
392 1
0.2%
391 1
0.2%
390 1
0.2%

age
Real number (ℝ)

MISSING 

Distinct76
Distinct (%)19.4%
Missing9
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean51.483376
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-11-20T16:09:02.671755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q142
median55
Q364.5
95-th percentile74.5
Maximum90
Range88
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation17.169714
Coefficient of variation (CV)0.33350016
Kurtosis0.057840495
Mean51.483376
Median Absolute Deviation (MAD)10
Skewness-0.66825947
Sum20130
Variance294.79908
MonotonicityNot monotonic
2023-11-20T16:09:02.872204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 19
 
4.8%
65 17
 
4.2%
48 12
 
3.0%
50 12
 
3.0%
55 12
 
3.0%
47 11
 
2.8%
56 10
 
2.5%
59 10
 
2.5%
45 10
 
2.5%
54 10
 
2.5%
Other values (66) 268
67.0%
ValueCountFrequency (%)
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 2
0.5%
6 1
 
0.2%
7 1
 
0.2%
8 3
0.8%
11 1
 
0.2%
12 2
0.5%
14 1
 
0.2%
ValueCountFrequency (%)
90 1
 
0.2%
83 1
 
0.2%
82 1
 
0.2%
81 1
 
0.2%
80 4
1.0%
79 1
 
0.2%
78 1
 
0.2%
76 5
1.2%
75 5
1.2%
74 3
0.8%

blood_pressure
Real number (ℝ)

MISSING 

Distinct10
Distinct (%)2.6%
Missing12
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean76.469072
Minimum50
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-11-20T16:09:03.057775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q170
median80
Q380
95-th percentile100
Maximum180
Range130
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.683637
Coefficient of variation (CV)0.17894342
Kurtosis8.6460952
Mean76.469072
Median Absolute Deviation (MAD)10
Skewness1.605429
Sum29670
Variance187.24194
MonotonicityNot monotonic
2023-11-20T16:09:03.310318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
80 116
29.0%
70 112
28.0%
60 71
17.8%
90 53
13.2%
100 25
 
6.2%
50 5
 
1.2%
110 3
 
0.8%
140 1
 
0.2%
180 1
 
0.2%
120 1
 
0.2%
(Missing) 12
 
3.0%
ValueCountFrequency (%)
50 5
 
1.2%
60 71
17.8%
70 112
28.0%
80 116
29.0%
90 53
13.2%
100 25
 
6.2%
110 3
 
0.8%
120 1
 
0.2%
140 1
 
0.2%
180 1
 
0.2%
ValueCountFrequency (%)
180 1
 
0.2%
140 1
 
0.2%
120 1
 
0.2%
110 3
 
0.8%
100 25
 
6.2%
90 53
13.2%
80 116
29.0%
70 112
28.0%
60 71
17.8%
50 5
 
1.2%

specific_gravity
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)1.4%
Missing47
Missing (%)11.8%
Memory size3.2 KiB
1.02
106 
1.01
84 
1.025
81 
1.015
75 
1.005
 
7

Length

Max length5
Median length4
Mean length4.4617564
Min length4

Characters and Unicode

Total characters1575
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.02
2nd row1.02
3rd row1.01
4th row1.005
5th row1.01

Common Values

ValueCountFrequency (%)
1.02 106
26.5%
1.01 84
21.0%
1.025 81
20.2%
1.015 75
18.8%
1.005 7
 
1.8%
(Missing) 47
11.8%

Length

2023-11-20T16:09:03.484160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:03.640436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1.02 106
30.0%
1.01 84
23.8%
1.025 81
22.9%
1.015 75
21.2%
1.005 7
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1222
77.6%
Other Punctuation 353
 
22.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 512
41.9%
0 360
29.5%
2 187
 
15.3%
5 163
 
13.3%
Other Punctuation
ValueCountFrequency (%)
. 353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1575
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1575
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 512
32.5%
0 360
22.9%
. 353
22.4%
2 187
 
11.9%
5 163
 
10.3%

albumin
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)1.7%
Missing46
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean1.0169492
Minimum0
Maximum5
Zeros199
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-11-20T16:09:03.796529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3526789
Coefficient of variation (CV)1.3301343
Kurtosis-0.3833766
Mean1.0169492
Median Absolute Deviation (MAD)0
Skewness0.99815724
Sum360
Variance1.8297402
MonotonicityNot monotonic
2023-11-20T16:09:03.936156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 199
49.8%
1 44
 
11.0%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
(Missing) 46
 
11.5%
ValueCountFrequency (%)
0 199
49.8%
1 44
 
11.0%
2 43
 
10.8%
3 43
 
10.8%
4 24
 
6.0%
5 1
 
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
4 24
 
6.0%
3 43
 
10.8%
2 43
 
10.8%
1 44
 
11.0%
0 199
49.8%

sugar
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)1.7%
Missing49
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean0.45014245
Minimum0
Maximum5
Zeros290
Zeros (%)72.5%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-11-20T16:09:04.080803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0991913
Coefficient of variation (CV)2.4418742
Kurtosis5.055348
Mean0.45014245
Median Absolute Deviation (MAD)0
Skewness2.4642618
Sum158
Variance1.2082214
MonotonicityNot monotonic
2023-11-20T16:09:04.228424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 290
72.5%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
1 13
 
3.2%
5 3
 
0.8%
(Missing) 49
 
12.2%
ValueCountFrequency (%)
0 290
72.5%
1 13
 
3.2%
2 18
 
4.5%
3 14
 
3.5%
4 13
 
3.2%
5 3
 
0.8%
ValueCountFrequency (%)
5 3
 
0.8%
4 13
 
3.2%
3 14
 
3.5%
2 18
 
4.5%
1 13
 
3.2%
0 290
72.5%

red_blood_cells
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.8%
Missing152
Missing (%)38.0%
Memory size3.2 KiB
normal
201 
abnormal
47 

Length

Max length8
Median length6
Mean length6.3790323
Min length6

Characters and Unicode

Total characters1582
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rownormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 201
50.2%
abnormal 47
 
11.8%
(Missing) 152
38.0%

Length

2023-11-20T16:09:04.422373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:04.566314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
normal 201
81.0%
abnormal 47
 
19.0%

Most occurring characters

ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1582
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1582
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 295
18.6%
n 248
15.7%
o 248
15.7%
r 248
15.7%
m 248
15.7%
l 248
15.7%
b 47
 
3.0%

pus_cell
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.6%
Missing65
Missing (%)16.2%
Memory size3.2 KiB
normal
259 
abnormal
76 

Length

Max length8
Median length6
Mean length6.4537313
Min length6

Characters and Unicode

Total characters2162
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rownormal
4th rowabnormal
5th rownormal

Common Values

ValueCountFrequency (%)
normal 259
64.8%
abnormal 76
 
19.0%
(Missing) 65
 
16.2%

Length

2023-11-20T16:09:04.734095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:04.903616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
normal 259
77.3%
abnormal 76
 
22.7%

Most occurring characters

ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2162
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 411
19.0%
n 335
15.5%
o 335
15.5%
r 335
15.5%
m 335
15.5%
l 335
15.5%
b 76
 
3.5%

pus_cell_clumps
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size3.2 KiB
notpresent
354 
present
42 

Length

Max length10
Median length10
Mean length9.6818182
Min length7

Characters and Unicode

Total characters3834
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rowpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent 354
88.5%
present 42
 
10.5%
(Missing) 4
 
1.0%

Length

2023-11-20T16:09:05.073197image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:05.221442image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
notpresent 354
89.4%
present 42
 
10.6%

Most occurring characters

ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3834
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 3834
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 792
20.7%
n 750
19.6%
t 750
19.6%
p 396
10.3%
r 396
10.3%
s 396
10.3%
o 354
9.2%

bacteria
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing4
Missing (%)1.0%
Memory size3.2 KiB
notpresent
374 
present
 
22

Length

Max length10
Median length10
Mean length9.8333333
Min length7

Characters and Unicode

Total characters3894
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownotpresent
2nd rownotpresent
3rd rownotpresent
4th rownotpresent
5th rownotpresent

Common Values

ValueCountFrequency (%)
notpresent 374
93.5%
present 22
 
5.5%
(Missing) 4
 
1.0%

Length

2023-11-20T16:09:05.398005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:05.578447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
notpresent 374
94.4%
present 22
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3894
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 3894
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 792
20.3%
n 770
19.8%
t 770
19.8%
p 396
10.2%
r 396
10.2%
s 396
10.2%
o 374
9.6%

blood_glucose_random
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct146
Distinct (%)41.0%
Missing44
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean148.03652
Minimum22
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-11-20T16:09:05.756673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile78.75
Q199
median121
Q3163
95-th percentile307.25
Maximum490
Range468
Interquartile range (IQR)64

Descriptive statistics

Standard deviation79.281714
Coefficient of variation (CV)0.53555512
Kurtosis4.2255936
Mean148.03652
Median Absolute Deviation (MAD)25
Skewness2.0107732
Sum52701
Variance6285.5902
MonotonicityNot monotonic
2023-11-20T16:09:05.964118image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 10
 
2.5%
93 9
 
2.2%
100 9
 
2.2%
107 8
 
2.0%
131 6
 
1.5%
140 6
 
1.5%
109 6
 
1.5%
92 6
 
1.5%
117 6
 
1.5%
130 6
 
1.5%
Other values (136) 284
71.0%
(Missing) 44
 
11.0%
ValueCountFrequency (%)
22 1
 
0.2%
70 5
1.2%
74 3
0.8%
75 2
 
0.5%
76 4
1.0%
78 3
0.8%
79 3
0.8%
80 2
 
0.5%
81 3
0.8%
82 3
0.8%
ValueCountFrequency (%)
490 2
0.5%
463 1
0.2%
447 1
0.2%
425 1
0.2%
424 2
0.5%
423 1
0.2%
415 1
0.2%
410 1
0.2%
380 1
0.2%
360 2
0.5%

blood_urea
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct118
Distinct (%)31.0%
Missing19
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean57.425722
Minimum1.5
Maximum391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-11-20T16:09:06.167323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile17
Q127
median42
Q366
95-th percentile162
Maximum391
Range389.5
Interquartile range (IQR)39

Descriptive statistics

Standard deviation50.503006
Coefficient of variation (CV)0.87944921
Kurtosis9.3452886
Mean57.425722
Median Absolute Deviation (MAD)16
Skewness2.6343745
Sum21879.2
Variance2550.5536
MonotonicityNot monotonic
2023-11-20T16:09:06.383290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 15
 
3.8%
25 13
 
3.2%
19 11
 
2.8%
40 10
 
2.5%
15 9
 
2.2%
48 9
 
2.2%
50 9
 
2.2%
18 9
 
2.2%
32 8
 
2.0%
49 8
 
2.0%
Other values (108) 280
70.0%
(Missing) 19
 
4.8%
ValueCountFrequency (%)
1.5 1
 
0.2%
10 2
 
0.5%
15 9
2.2%
16 7
1.8%
17 7
1.8%
18 9
2.2%
19 11
2.8%
20 7
1.8%
21 1
 
0.2%
22 6
1.5%
ValueCountFrequency (%)
391 1
0.2%
322 1
0.2%
309 1
0.2%
241 1
0.2%
235 1
0.2%
223 1
0.2%
219 1
0.2%
217 1
0.2%
215 1
0.2%
208 1
0.2%

serum_creatinine
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)21.9%
Missing17
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean3.0724543
Minimum0.4
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-11-20T16:09:06.590770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.5
Q10.9
median1.3
Q32.8
95-th percentile11.89
Maximum76
Range75.6
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation5.7411261
Coefficient of variation (CV)1.8685798
Kurtosis79.304345
Mean3.0724543
Median Absolute Deviation (MAD)0.6
Skewness7.5095383
Sum1176.75
Variance32.960529
MonotonicityNot monotonic
2023-11-20T16:09:06.800353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 40
 
10.0%
1.1 24
 
6.0%
0.5 23
 
5.8%
1 23
 
5.8%
0.9 22
 
5.5%
0.7 22
 
5.5%
0.6 18
 
4.5%
0.8 17
 
4.2%
2.2 10
 
2.5%
1.5 9
 
2.2%
Other values (74) 175
43.8%
(Missing) 17
 
4.2%
ValueCountFrequency (%)
0.4 1
 
0.2%
0.5 23
5.8%
0.6 18
4.5%
0.7 22
5.5%
0.8 17
4.2%
0.9 22
5.5%
1 23
5.8%
1.1 24
6.0%
1.2 40
10.0%
1.3 8
 
2.0%
ValueCountFrequency (%)
76 1
0.2%
48.1 1
0.2%
32 1
0.2%
24 1
0.2%
18.1 1
0.2%
18 1
0.2%
16.9 1
0.2%
16.4 1
0.2%
15.2 1
0.2%
15 1
0.2%

sodium
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)10.9%
Missing87
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean137.52875
Minimum4.5
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-11-20T16:09:06.988668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile125
Q1135
median138
Q3142
95-th percentile150
Maximum163
Range158.5
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.408752
Coefficient of variation (CV)0.075684188
Kurtosis85.53437
Mean137.52875
Median Absolute Deviation (MAD)3
Skewness-6.9965686
Sum43046.5
Variance108.34212
MonotonicityNot monotonic
2023-11-20T16:09:07.170970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
135 40
10.0%
140 25
 
6.2%
141 22
 
5.5%
139 21
 
5.2%
142 20
 
5.0%
138 20
 
5.0%
137 19
 
4.8%
150 17
 
4.2%
136 17
 
4.2%
147 13
 
3.2%
Other values (24) 99
24.8%
(Missing) 87
21.8%
ValueCountFrequency (%)
4.5 1
 
0.2%
104 1
 
0.2%
111 1
 
0.2%
113 2
0.5%
114 2
0.5%
115 1
 
0.2%
120 2
0.5%
122 2
0.5%
124 3
0.8%
125 2
0.5%
ValueCountFrequency (%)
163 1
 
0.2%
150 17
4.2%
147 13
3.2%
146 10
 
2.5%
145 11
2.8%
144 9
 
2.2%
143 4
 
1.0%
142 20
5.0%
141 22
5.5%
140 25
6.2%

potassium
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)12.8%
Missing88
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean4.6272436
Minimum2.5
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-11-20T16:09:07.356733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.4
Q13.8
median4.4
Q34.9
95-th percentile5.7
Maximum47
Range44.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation3.1939042
Coefficient of variation (CV)0.69023904
Kurtosis142.50591
Mean4.6272436
Median Absolute Deviation (MAD)0.5
Skewness11.582956
Sum1443.7
Variance10.201024
MonotonicityNot monotonic
2023-11-20T16:09:07.534447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3.5 30
 
7.5%
5 30
 
7.5%
4.9 27
 
6.8%
4.7 17
 
4.2%
4.8 16
 
4.0%
4 14
 
3.5%
4.1 14
 
3.5%
4.4 14
 
3.5%
3.9 14
 
3.5%
3.8 14
 
3.5%
Other values (30) 122
30.5%
(Missing) 88
22.0%
ValueCountFrequency (%)
2.5 2
 
0.5%
2.7 1
 
0.2%
2.8 1
 
0.2%
2.9 3
 
0.8%
3 2
 
0.5%
3.2 3
 
0.8%
3.3 3
 
0.8%
3.4 5
 
1.2%
3.5 30
7.5%
3.6 8
 
2.0%
ValueCountFrequency (%)
47 1
 
0.2%
39 1
 
0.2%
7.6 1
 
0.2%
6.6 1
 
0.2%
6.5 2
0.5%
6.4 1
 
0.2%
6.3 3
0.8%
5.9 2
0.5%
5.8 2
0.5%
5.7 4
1.0%

hemoglobin
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)33.0%
Missing52
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean12.526437
Minimum3.1
Maximum17.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-11-20T16:09:07.716693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile7.9
Q110.3
median12.65
Q315
95-th percentile16.9
Maximum17.8
Range14.7
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation2.9125866
Coefficient of variation (CV)0.23251517
Kurtosis-0.47139804
Mean12.526437
Median Absolute Deviation (MAD)2.35
Skewness-0.33509468
Sum4359.2
Variance8.4831608
MonotonicityNot monotonic
2023-11-20T16:09:07.919226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 16
 
4.0%
10.9 8
 
2.0%
13.6 7
 
1.8%
13 7
 
1.8%
9.8 7
 
1.8%
11.1 7
 
1.8%
10.3 6
 
1.5%
11.3 6
 
1.5%
13.9 6
 
1.5%
12 6
 
1.5%
Other values (105) 272
68.0%
(Missing) 52
 
13.0%
ValueCountFrequency (%)
3.1 1
0.2%
4.8 1
0.2%
5.5 1
0.2%
5.6 1
0.2%
5.8 1
0.2%
6 2
0.5%
6.1 1
0.2%
6.2 1
0.2%
6.3 1
0.2%
6.6 1
0.2%
ValueCountFrequency (%)
17.8 3
0.8%
17.7 1
 
0.2%
17.6 1
 
0.2%
17.5 1
 
0.2%
17.4 2
0.5%
17.3 1
 
0.2%
17.2 2
0.5%
17.1 2
0.5%
17 4
1.0%
16.9 2
0.5%

packed_cell_volume
Categorical

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)13.0%
Missing70
Missing (%)17.5%
Memory size3.2 KiB
41
 
21
52
 
21
48
 
19
44
 
19
40
 
16
Other values (38)
234 

Length

Max length2
Median length2
Mean length1.9939394
Min length1

Characters and Unicode

Total characters658
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)2.7%

Sample

1st row44
2nd row38
3rd row31
4th row32
5th row35

Common Values

ValueCountFrequency (%)
41 21
 
5.2%
52 21
 
5.2%
48 19
 
4.8%
44 19
 
4.8%
40 16
 
4.0%
43 15
 
3.8%
42 13
 
3.2%
45 13
 
3.2%
50 12
 
3.0%
32 12
 
3.0%
Other values (33) 169
42.2%
(Missing) 70
17.5%

Length

2023-11-20T16:09:08.113162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
41 21
 
6.4%
52 21
 
6.4%
48 19
 
5.8%
44 19
 
5.8%
40 16
 
4.8%
43 15
 
4.5%
42 13
 
3.9%
45 13
 
3.9%
36 12
 
3.6%
28 12
 
3.6%
Other values (33) 169
51.2%

Most occurring characters

ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 657
99.8%
Other Punctuation 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 175
26.6%
3 129
19.6%
2 96
14.6%
5 71
10.8%
1 41
 
6.2%
0 38
 
5.8%
8 37
 
5.6%
6 28
 
4.3%
9 23
 
3.5%
7 19
 
2.9%
Distinct90
Distinct (%)30.5%
Missing105
Missing (%)26.2%
Memory size3.2 KiB
2023-11-20T16:09:08.364982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.2169492
Min length1

Characters and Unicode

Total characters1244
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)10.8%

Sample

1st row7800
2nd row6000
3rd row7500
4th row6700
5th row7300
ValueCountFrequency (%)
9800 11
 
3.7%
6700 10
 
3.4%
9600 9
 
3.1%
7200 9
 
3.1%
9200 9
 
3.1%
6900 8
 
2.7%
5800 8
 
2.7%
11000 8
 
2.7%
9100 7
 
2.4%
9400 7
 
2.4%
Other values (80) 209
70.8%
2023-11-20T16:09:08.814800image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 645
51.8%
1 99
 
8.0%
9 75
 
6.0%
6 75
 
6.0%
7 75
 
6.0%
8 67
 
5.4%
5 66
 
5.3%
2 55
 
4.4%
4 50
 
4.0%
3 36
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1243
99.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 645
51.9%
1 99
 
8.0%
9 75
 
6.0%
6 75
 
6.0%
7 75
 
6.0%
8 67
 
5.4%
5 66
 
5.3%
2 55
 
4.4%
4 50
 
4.0%
3 36
 
2.9%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1244
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 645
51.8%
1 99
 
8.0%
9 75
 
6.0%
6 75
 
6.0%
7 75
 
6.0%
8 67
 
5.4%
5 66
 
5.3%
2 55
 
4.4%
4 50
 
4.0%
3 36
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 645
51.8%
1 99
 
8.0%
9 75
 
6.0%
6 75
 
6.0%
7 75
 
6.0%
8 67
 
5.4%
5 66
 
5.3%
2 55
 
4.4%
4 50
 
4.0%
3 36
 
2.9%

red_blood_cell_count
Categorical

HIGH CORRELATION  MISSING 

Distinct49
Distinct (%)18.1%
Missing130
Missing (%)32.5%
Memory size3.2 KiB
5.2
 
18
4.5
 
16
4.9
 
14
4.7
 
11
3.9
 
10
Other values (44)
201 

Length

Max length3
Median length3
Mean length2.9481481
Min length1

Characters and Unicode

Total characters796
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)1.9%

Sample

1st row5.2
2nd row3.9
3rd row4.6
4th row4.4
5th row5

Common Values

ValueCountFrequency (%)
5.2 18
 
4.5%
4.5 16
 
4.0%
4.9 14
 
3.5%
4.7 11
 
2.8%
3.9 10
 
2.5%
4.8 10
 
2.5%
4.6 9
 
2.2%
3.4 9
 
2.2%
6.1 8
 
2.0%
3.7 8
 
2.0%
Other values (39) 157
39.2%
(Missing) 130
32.5%

Length

2023-11-20T16:09:09.025047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5.2 18
 
6.7%
4.5 16
 
5.9%
4.9 14
 
5.2%
4.7 11
 
4.1%
3.9 10
 
3.7%
4.8 10
 
3.7%
4.6 9
 
3.3%
3.4 9
 
3.3%
5.0 8
 
3.0%
5.9 8
 
3.0%
Other values (39) 157
58.1%

Most occurring characters

ValueCountFrequency (%)
. 263
33.0%
5 115
14.4%
4 115
14.4%
3 75
 
9.4%
6 52
 
6.5%
2 48
 
6.0%
9 34
 
4.3%
8 27
 
3.4%
7 26
 
3.3%
1 22
 
2.8%
Other values (2) 19
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 532
66.8%
Other Punctuation 264
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 115
21.6%
4 115
21.6%
3 75
14.1%
6 52
9.8%
2 48
9.0%
9 34
 
6.4%
8 27
 
5.1%
7 26
 
4.9%
1 22
 
4.1%
0 18
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 263
99.6%
? 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 796
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 263
33.0%
5 115
14.4%
4 115
14.4%
3 75
 
9.4%
6 52
 
6.5%
2 48
 
6.0%
9 34
 
4.3%
8 27
 
3.4%
7 26
 
3.3%
1 22
 
2.8%
Other values (2) 19
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 796
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 263
33.0%
5 115
14.4%
4 115
14.4%
3 75
 
9.4%
6 52
 
6.5%
2 48
 
6.0%
9 34
 
4.3%
8 27
 
3.4%
7 26
 
3.3%
1 22
 
2.8%
Other values (2) 19
 
2.4%

hypertension
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size928.0 B
False
251 
True
147 
(Missing)
 
2
ValueCountFrequency (%)
False 251
62.7%
True 147
36.8%
(Missing) 2
 
0.5%
2023-11-20T16:09:09.179137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

diabetes_mellitus
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size928.0 B
False
261 
True
137 
(Missing)
 
2
ValueCountFrequency (%)
False 261
65.2%
True 137
34.2%
(Missing) 2
 
0.5%
2023-11-20T16:09:09.314235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

coronary_artery_disease
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing2
Missing (%)0.5%
Memory size928.0 B
False
364 
True
 
34
(Missing)
 
2
ValueCountFrequency (%)
False 364
91.0%
True 34
 
8.5%
(Missing) 2
 
0.5%
2023-11-20T16:09:09.445916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

appet
Categorical

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size3.2 KiB
good
317 
poor
82 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1596
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgood
2nd rowgood
3rd rowpoor
4th rowpoor
5th rowgood

Common Values

ValueCountFrequency (%)
good 317
79.2%
poor 82
 
20.5%
(Missing) 1
 
0.2%

Length

2023-11-20T16:09:09.589630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:09.724878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
good 317
79.4%
poor 82
 
20.6%

Most occurring characters

ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1596
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1596
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 798
50.0%
g 317
 
19.9%
d 317
 
19.9%
p 82
 
5.1%
r 82
 
5.1%
Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size928.0 B
False
323 
True
76 
(Missing)
 
1
ValueCountFrequency (%)
False 323
80.8%
True 76
 
19.0%
(Missing) 1
 
0.2%
2023-11-20T16:09:09.861623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

anemia
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing1
Missing (%)0.2%
Memory size928.0 B
False
339 
True
60 
(Missing)
 
1
ValueCountFrequency (%)
False 339
84.8%
True 60
 
15.0%
(Missing) 1
 
0.2%
2023-11-20T16:09:09.991324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

classification
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
1
250 
0
150 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters400
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 250
62.5%
0 150
37.5%

Length

2023-11-20T16:09:10.137644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T16:09:10.279577image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 250
62.5%
0 150
37.5%

Most occurring characters

ValueCountFrequency (%)
1 250
62.5%
0 150
37.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 250
62.5%
0 150
37.5%

Most occurring scripts

ValueCountFrequency (%)
Common 400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 250
62.5%
0 150
37.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 250
62.5%
0 150
37.5%

Interactions

2023-11-20T16:08:58.819301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:41.931598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:43.741758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:45.316865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:47.105887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:49.042851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:50.926175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:52.798814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:54.553528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:56.042115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:57.492454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:58.959393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:42.099304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:43.882072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:45.495166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:47.251422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:49.534373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:51.087103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:52.966366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:54.695615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:56.183948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:57.620278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:59.076291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:42.233468image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:43.993865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:45.687969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:47.372558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:49.650278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:51.217158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:53.109949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:54.827090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:56.311349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:57.727767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:59.212476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:42.407959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:44.167183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:45.871880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:47.528896image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:49.777494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:51.360417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:53.261187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:54.964152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:56.448982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:57.848444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:59.365102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:42.629430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:44.311158image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:46.032159image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:47.663198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:49.896716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:51.504937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:53.399814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:55.082355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:56.583883image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:57.958494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:59.497716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:42.764507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:44.444669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:46.183311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:47.796466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:50.029157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:51.997692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:53.547453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:55.212950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:56.710847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:58.074184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:59.623378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:42.918593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:44.562474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:46.345019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:47.984236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:50.169556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:52.146931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:53.694640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:55.348619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:56.839252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:58.185303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:59.751728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:43.097996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:44.731994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:46.498969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:48.159025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:50.333119image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:52.297111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:53.879144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:55.488891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:56.975799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:58.313803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:59.883373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:43.250886image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:44.890654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:46.644651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:48.318009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:50.495739image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:52.425003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:54.036723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:55.623530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:57.123428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:58.431161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:00.030344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:43.389250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:45.052325image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:46.834416image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:48.594358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:50.647739image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:52.548712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:54.290068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:55.768816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:57.256866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:58.547314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:09:00.144601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:43.579152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:45.160424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:46.952543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:48.793221image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:50.769297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:52.662034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:54.421715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:55.896706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:57.363797image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-20T16:08:58.661707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-20T16:09:10.423164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
idageblood_pressurealbuminsugarblood_glucose_randomblood_ureaserum_creatininesodiumpotassiumhemoglobinspecific_gravityred_blood_cellspus_cellpus_cell_clumpsbacteriapacked_cell_volumered_blood_cell_counthypertensiondiabetes_mellituscoronary_artery_diseaseappetpedal_edemaanemiaclassification
id1.000-0.217-0.248-0.611-0.302-0.351-0.343-0.6010.489-0.0300.6820.3820.5260.4040.2390.1560.2800.2740.5490.5270.1880.3970.3320.2630.949
age-0.2171.0000.1230.2130.2810.2990.3090.350-0.1340.072-0.2300.0880.0640.1330.1800.0730.0690.0370.3920.3670.2080.1290.1160.1270.349
blood_pressure-0.2480.1231.0000.1950.2170.1770.1840.305-0.1370.091-0.2760.1700.3900.2900.1570.1270.4160.4320.3570.2840.0000.2540.1600.2860.442
albumin-0.6110.2130.1951.0000.3580.3720.4950.641-0.5340.053-0.6830.2870.5330.5880.4530.4100.3850.4120.5490.4580.3320.3770.4740.3440.726
sugar-0.3020.2810.2170.3581.0000.6020.2230.356-0.2290.055-0.2960.1830.2130.2220.1970.1860.2400.2730.3700.5490.3820.2570.1650.1450.366
blood_glucose_random-0.3510.2990.1770.3720.6021.0000.1950.359-0.2610.072-0.3490.2090.3640.3870.1760.1040.0000.1910.4460.5760.3060.2500.2070.1350.459
blood_urea-0.3430.3090.1840.4950.2230.1951.0000.703-0.4140.212-0.5920.1970.3220.4080.2070.2300.4570.4090.4710.3650.2780.2650.3180.4540.381
serum_creatinine-0.6010.3500.3050.6410.3560.3590.7031.000-0.4970.129-0.7260.1390.2090.2410.0000.0000.3830.4770.1800.1790.1430.1410.2720.3750.185
sodium0.489-0.134-0.137-0.534-0.229-0.261-0.414-0.4971.0000.0210.5110.2320.2920.3450.2610.1600.2560.3110.3640.3010.2110.2400.2160.3280.396
potassium-0.0300.0720.0910.0530.0550.0720.2120.1290.0211.000-0.0630.0390.0000.1850.0000.0000.5110.5500.0590.0160.0000.0750.1350.1680.000
hemoglobin0.682-0.230-0.276-0.683-0.296-0.349-0.592-0.7260.511-0.0631.0000.3220.4890.5520.3680.2320.6910.4700.6050.5210.2710.4310.4360.6900.846
specific_gravity0.3820.0880.1700.2870.1830.2090.1970.1390.2320.0390.3221.0000.4350.3850.2840.2040.2960.3880.4190.4500.1580.2740.3520.2490.789
red_blood_cells0.5260.0640.3900.5330.2130.3640.3220.2090.2920.0000.4890.4351.0000.4100.0690.1480.5390.4380.2890.3210.1610.2620.2820.1630.542
pus_cell0.4040.1330.2900.5880.2220.3870.4080.2410.3450.1850.5520.3850.4101.0000.5010.3110.5750.5690.3720.2890.1940.3030.4030.3150.452
pus_cell_clumps0.2390.1800.1570.4530.1970.1760.2070.0000.2610.0000.3680.2840.0690.5011.0000.2520.3480.3340.1770.1450.1650.1710.0770.1550.250
bacteria0.1560.0730.1270.4100.1860.1040.2300.0000.1600.0000.2320.2040.1480.3110.2521.0000.2210.2550.0560.0430.1330.1250.1080.0000.167
packed_cell_volume0.2800.0690.4160.3850.2400.0000.4570.3830.2560.5110.6910.2960.5390.5750.3480.2211.0000.3210.6050.5220.3900.4550.4660.6200.773
red_blood_cell_count0.2740.0370.4320.4120.2730.1910.4090.4770.3110.5500.4700.3880.4380.5690.3340.2550.3211.0000.6460.5520.5450.4690.4690.5940.730
hypertension0.5490.3920.3570.5490.3700.4460.4710.1800.3640.0590.6050.4190.2890.3720.1770.0560.6050.6461.0000.6000.3120.3330.3600.3360.582
diabetes_mellitus0.5270.3670.2840.4580.5490.5760.3650.1790.3010.0160.5210.4500.3210.2890.1450.0430.5220.5520.6001.0000.2560.3130.2960.1670.550
coronary_artery_disease0.1880.2080.0000.3320.3820.3060.2780.1430.2110.0000.2710.1580.1610.1940.1650.1330.3900.5450.3120.2561.0000.1350.1520.0000.220
appet0.3970.1290.2540.3770.2570.2500.2650.1410.2400.0750.4310.2740.2620.3030.1710.1250.4550.4690.3330.3130.1351.0000.4060.2410.383
pedal_edema0.3320.1160.1600.4740.1650.2070.3180.2720.2160.1350.4360.3520.2820.4030.0770.1080.4660.4690.3600.2960.1520.4061.0000.1910.365
anemia0.2630.1270.2860.3440.1450.1350.4540.3750.3280.1680.6900.2490.1630.3150.1550.0000.6200.5940.3360.1670.0000.2410.1911.0000.314
classification0.9490.3490.4420.7260.3660.4590.3810.1850.3960.0000.8460.7890.5420.4520.2500.1670.7730.7300.5820.5500.2200.3830.3650.3141.000

Missing values

2023-11-20T16:09:00.378578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-20T16:09:00.835948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-20T16:09:01.301281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idageblood_pressurespecific_gravityalbuminsugarred_blood_cellspus_cellpus_cell_clumpsbacteriablood_glucose_randomblood_ureaserum_creatininesodiumpotassiumhemoglobinpacked_cell_volumewhite_blood_cell_countred_blood_cell_counthypertensiondiabetes_mellituscoronary_artery_diseaseappetpedal_edemaanemiaclassification
0048.080.01.0201.00.0NaNnormalnotpresentnotpresent121.036.01.2NaNNaN15.44478005.2yesyesnogoodnono1
117.050.01.0204.00.0NaNnormalnotpresentnotpresentNaN18.00.8NaNNaN11.3386000NaNnononogoodnono1
2262.080.01.0102.03.0normalnormalnotpresentnotpresent423.053.01.8NaNNaN9.6317500NaNnoyesnopoornoyes1
3348.070.01.0054.00.0normalabnormalpresentnotpresent117.056.03.8111.02.511.23267003.9yesnonopooryesyes1
4451.080.01.0102.00.0normalnormalnotpresentnotpresent106.026.01.4NaNNaN11.63573004.6nononogoodnono1
5560.090.01.0153.00.0NaNNaNnotpresentnotpresent74.025.01.1142.03.212.23978004.4yesyesnogoodyesno1
6668.070.01.0100.00.0NaNnormalnotpresentnotpresent100.054.024.0104.04.012.436NaNNaNnononogoodnono1
7724.0NaN1.0152.04.0normalabnormalnotpresentnotpresent410.031.01.1NaNNaN12.44469005noyesnogoodyesno1
8852.0100.01.0153.00.0normalabnormalpresentnotpresent138.060.01.9NaNNaN10.83396004.0yesyesnogoodnoyes1
9953.090.01.0202.00.0abnormalabnormalpresentnotpresent70.0107.07.2114.03.79.529121003.7yesyesnopoornoyes1
idageblood_pressurespecific_gravityalbuminsugarred_blood_cellspus_cellpus_cell_clumpsbacteriablood_glucose_randomblood_ureaserum_creatininesodiumpotassiumhemoglobinpacked_cell_volumewhite_blood_cell_countred_blood_cell_counthypertensiondiabetes_mellituscoronary_artery_diseaseappetpedal_edemaanemiaclassification
39039052.080.01.0250.00.0normalnormalnotpresentnotpresent99.025.00.8135.03.715.05263005.3nononogoodnono0
39139136.080.01.0250.00.0normalnormalnotpresentnotpresent85.016.01.1142.04.115.64458006.3nononogoodnono0
39239257.080.01.0200.00.0normalnormalnotpresentnotpresent133.048.01.2147.04.314.84666005.5nononogoodnono0
39339343.060.01.0250.00.0normalnormalnotpresentnotpresent117.045.00.7141.04.413.05474005.4nononogoodnono0
39439450.080.01.0200.00.0normalnormalnotpresentnotpresent137.046.00.8139.05.014.14595004.6nononogoodnono0
39539555.080.01.0200.00.0normalnormalnotpresentnotpresent140.049.00.5150.04.915.74767004.9nononogoodnono0
39639642.070.01.0250.00.0normalnormalnotpresentnotpresent75.031.01.2141.03.516.55478006.2nononogoodnono0
39739712.080.01.0200.00.0normalnormalnotpresentnotpresent100.026.00.6137.04.415.84966005.4nononogoodnono0
39839817.060.01.0250.00.0normalnormalnotpresentnotpresent114.050.01.0135.04.914.25172005.9nononogoodnono0
39939958.080.01.0250.00.0normalnormalnotpresentnotpresent131.018.01.1141.03.515.85368006.1nononogoodnono0